Palombo, M;
Hill, I;
Santin, MD;
Branzoli, F;
Philippe, A-C;
Wassermann, D;
Aigrot, M-S;
... Drobnjak, I; + view all
(2018)
Machine learning based estimation of axonal permeability: validation on cuprizone treated in-vivo mouse model of axonal demyelination.
In: Miller, KL and Port, JD, (eds.)
Proceedings of the Joint Annual Meeting ISMRM-ESMRMB 2018.
ISMRM (International Society for Magnetic Resonance in Medicine): Concord, CA, USA.
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Abstract
Estimating axonal permeability reliably is extremely important, however not yet achieved because mathematical models that express its relationship to the MR signal accurately are intractable. Recently introduced machine learning based computational model showed to outperforms previous approximate mathematical models. Here we apply and validate this novel method experimentally on a highly controlled in-vivo mouse model of axonal demyelination, and demonstrate for the first time in practice the power of machine learning as a mechanism to construct complex biophysical models for quantitative MRI.
| Type: | Proceedings paper |
|---|---|
| Title: | Machine learning based estimation of axonal permeability: validation on cuprizone treated in-vivo mouse model of axonal demyelination |
| Event: | Joint Annual Meeting ISMRM-ESMRMB 2018, 16-21 June 2018, Paris, France |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://www.ismrm.org/18m/ |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Chemical Engineering UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10074389 |
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